AI in plastic surgery: customizing care for each patient

Camille Brenac , Alexander Z. Fazilat , Mahsa Fallah , Danae Kawamoto-Duran , Parker S. Sunwoo , Michael T. Longaker , Derrick C. Wan , Jason L. Guo

Artificial Intelligence Surgery ›› 2024, Vol. 4 ›› Issue (4) : 296 -315.

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Artificial Intelligence Surgery ›› 2024, Vol. 4 ›› Issue (4) :296 -315. DOI: 10.20517/ais.2024.49
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AI in plastic surgery: customizing care for each patient

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Abstract

Artificial intelligence (AI) and machine learning (ML) involve the usage of complex algorithms to identify patterns, predict future outcomes, generate new data, and perform other tasks that typically require human intelligence. AI tools have been progressively adopted by multiple disciplines of surgery, enabling increasingly patient-specific care, as well as more precise surgical modeling and assessment. For instance, AI tools such as ChatGPT have been applied to enhance both patient educational materials and patient-surgeon communication. Additionally, AI tools have helped support pre- and postoperative assessment in a diverse set of procedures, including breast reconstructions, facial surgeries, hand surgeries, wound healing operations, and burn surgeries. Further, ML-supported 3D modeling has now been utilized for patient-specific surgical planning and may also be combined with 3D printing technologies to generate patient-customized, implantable constructs. Ultimately, the advent of AI and its intersection with surgical practice have demonstrated immense potential to transform patient care by making multiple facets of the surgical process more efficient, precise, and patient-specific.

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Plastic surgery / machine learning / artificial intelligence / algorithms

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Camille Brenac, Alexander Z. Fazilat, Mahsa Fallah, Danae Kawamoto-Duran, Parker S. Sunwoo, Michael T. Longaker, Derrick C. Wan, Jason L. Guo. AI in plastic surgery: customizing care for each patient. Artificial Intelligence Surgery, 2024, 4(4): 296-315 DOI:10.20517/ais.2024.49

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References

[1]

Maita KC,Torres-Guzman RA.The usefulness of artificial intelligence in breast reconstruction: a systematic review.Breast Cancer2024;31:562-71

[2]

Guo JL,Longaker MT.Machine learning in tissue engineering.Tissue Eng Part A2023;29:2-19 PMCID:PMC9885550

[3]

Choi E,Jassal JS,Ramachandra V.Artificial intelligence in facial plastic surgery: a review of current applications, future applications, and ethical considerations.Facial Plast Surg2023;39:454-9

[4]

Sacristán JA.No big data without small data: learning health care systems begin and end with the individual patient.J Eval Clin Pract2015;21:1014-7 PMCID:PMC6680345

[5]

Hume KM,Simmons CJ,Chung KC.Medical specialty society-sponsored data registries: opportunities in plastic surgery.Plast Reconstr Surg2013;132:159e-67e PMCID:PMC4164154

[6]

Davenport T.The potential for artificial intelligence in healthcare.Future Healthc J2019;6:94-8 PMCID:PMC6616181

[7]

Phillips M,Jaffe W.Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions.JAMA Netw Open2019;2:e1913436 PMCID:PMC6806667

[8]

Joshi G,Araveeti SR,Garg H.FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape.Electronics2024;13:498

[9]

Esteva A,Novoa RA.Dermatologist-level classification of skin cancer with deep neural networks.Nature2017;542:115-8 PMCID:PMC8382232

[10]

Koh DM,Bick U.Artificial intelligence and machine learning in cancer imaging.Commun Med2022;2:133 PMCID:PMC9613681

[11]

Barreiro-Ares A,Sendra-Portero F.Impact of the rise of artificial intelligence in radiology: what do students think?.Int J Environ Res Public Health2023;20:1589 PMCID:PMC9867061

[12]

Shin HJ,Ryu L.The impact of artificial intelligence on the reading times of radiologists for chest radiographs.NPJ Digit Med2023;6:82 PMCID:PMC10148851

[13]

Yacoub B,Schoepf UJ.Impact of artificial intelligence assistance on chest CT interpretation times: a prospective randomized study.AJR Am J Roentgenol2022;219:743-51

[14]

Farid Y,Ortiz S.Artificial intelligence in plastic surgery: insights from plastic surgeons, education integration, ChatGPT’s survey predictions, and the path forward.Plast Reconstr Surg Glob Open2024;12:e5515 PMCID:PMC10781127

[15]

Duong TV,Hung TNK.Artificial intelligence in plastic surgery: advancements, applications, and future.Cosmetics2024;11:109

[16]

Ho AL,Cano S,Pusic AL.Optimizing patient-centered care in breast reconstruction: the importance of preoperative information and patient-physician communication.Plast Reconstr Surg2013;132:212e-20e

[17]

Browne R,Hurley CM,O’Sullivan JB.ChatGPT-4 can help hand surgeons communicate better with patients.J Hand Surg Glob Online2024;6:436-8 PMCID:PMC11133925

[18]

Berry CE,Churukian AA.Quality assessment of online resources for gender-affirming surgery.Plast Reconstr Surg Glob Open2023;11:e5306 PMCID:PMC10561794

[19]

Baldwin AJ.An artificial intelligence language model improves readability of burns first aid information.Burns2024;50:1122-7

[20]

Berry CE,Lavin C.Both patients and plastic surgeons prefer artificial intelligence-generated microsurgical information. J Reconstr Microsurg 2024;40:657-64.

[21]

Grippaudo FR,Patrignani A.Quality of the information provided by ChatGPT for patients in breast plastic surgery: are we already in the future?.JPRAS Open2024;40:99-105 PMCID:PMC10914413

[22]

Seth I,Xie Y.Evaluating chatbot efficacy for answering frequently asked questions in plastic surgery: a ChatGPT case study focused on breast augmentation.Aesthet Surg J2023;43:1126-35

[23]

Xie Y,Hunter-Smith DJ,Ross R.Aesthetic surgery advice and counseling from artificial intelligence: a rhinoplasty consultation with ChatGPT.Aesthetic Plast Surg2023;47:1985-93 PMCID:PMC10581928

[24]

Chaker SC,Saad M,Galdyn IA.Easing the burden on caregivers- applications of artificial intelligence for physicians and caregivers of children with cleft lip and palate. Cleft Palate Craniofac J 2024.

[25]

Sharma SC,Thakker A.ChatGPT in plastic and reconstructive surgery.Indian J Plast Surg2023;56:320-5 PMCID:PMC10497341

[26]

Altamimi I,Alhumimidi AS,Temsah MH.Artificial intelligence (AI) chatbots in medicine: a supplement, not a substitute.Cureus2023;15:e40922 PMCID:PMC10367431

[27]

Ahmed SK,Aziz TA,Islam MR.The power of ChatGPT in revolutionizing rural healthcare delivery.Health Sci Rep2023;6:e1684 PMCID:PMC10620374

[28]

Wang A,Oleru O,Taub PJ.Artificial intelligence in plastic surgery: ChatGPT as a tool to address disparities in health literacy.Plast Reconstr Surg2024;153:1232e-4e PMCID:PMC11090984

[29]

Daraz L,Ponce OJ.Can patients trust online health information? A meta-narrative systematic review addressing the quality of health information on the internet.J Gen Intern Med2019;34:1884-91 PMCID:PMC6712138

[30]

Shahsavar Y.User intentions to use ChatGPT for self-diagnosis and health-related purposes: cross-sectional survey study.JMIR Hum Factors2023;10:e47564 PMCID:PMC10233444

[31]

Fazilat AZ,Churukian A.AI-based cleft lip and palate surgical information is preferred by both plastic surgeons and patients in a blind comparison. Cleft Palate Cran J 2024.

[32]

Boczar D,Oliver JD.Artificial intelligent virtual assistant for plastic surgery patient’s frequently asked questions: a pilot study.Ann Plast Surg2020;84:e16-21

[33]

Soh CL,Arjomandi Rad A.Present and future of machine learning in breast surgery: systematic review.Br J Surg2022;109:1053-62 PMCID:PMC10364755

[34]

Mavioso C,Oliveira HP.Automatic detection of perforators for microsurgical reconstruction.Breast2020;50:19-24 PMCID:PMC7375543

[35]

Kiranantawat K,Taeprasartsit P.The first smartphone application for microsurgery monitoring: SilpaRamanitor.Plast Reconstr Surg2014;134:130-9

[36]

Myung Y,Heo C.Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study.Sci Rep2021;11:5615 PMCID:PMC7946880

[37]

Hassan AM,Asaad M.Artificial intelligence modeling to predict periprosthetic infection and explantation following implant-based reconstruction.Plast Reconstr Surg2023;152:929-38

[38]

Bennett SP,Berry MG,Salmon RJ.Management of exposed, infected implant-based breast reconstruction and strategies for salvage.J Plast Reconstr Aesthet Surg2011;64:1270-7

[39]

Zhang BH,Lu SM.Turning back the clock: artificial intelligence recognition of age reduction after face-lift surgery correlates with patient satisfaction.Plast Reconstr Surg2021;148:45-54

[40]

Boonipat T,Lin J,Mardini S.Using artificial intelligence to measure facial expression following facial reanimation surgery.Plast Reconstr Surg2020;146:1147-50

[41]

Geisler EL,Hallac RR,Kane AA.A role for artificial intelligence in the classification of craniofacial anomalies.J Craniofac Surg2021;32:967-9

[42]

Knoops PGM,Borghi A.A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery.Sci Rep2019;9:13597 PMCID:PMC6753131

[43]

Marcus G,Aaronson S. A very preliminary analysis of DALL-E 2. arXiv. [Preprint.] May 2, 2022 [accessed on 2024 Sep 30]. Available from: https://doi.org/10.48550/arXiv.2204.13807.

[44]

Lim B,Kah S.Using generative artificial intelligence tools in cosmetic surgery: a study on rhinoplasty, facelifts, and blepharoplasty procedures.J Clin Med2023;12:6524 PMCID:PMC10607912

[45]

Bäcker HC,Strauch RJ.Systematic review of diagnosis of clinically suspected scaphoid fractures.J Wrist Surg2020;9:81-9 PMCID:PMC7000269

[46]

Ozkaya E,Bulut T,Ozuysal M.Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography.Eur J Trauma Emerg Surg2022;48:585-92

[47]

Oeding JF,Messer CJ.Diagnostic performance of artificial intelligence for detection of scaphoid and distal radius fractures: a systematic review.J Hand Surg Am2024;49:411-22

[48]

Hoogendam L,Souer JS,Andrinopoulou ER.Hand Wrist Study GroupPredicting clinically relevant patient-reported symptom improvement after carpal tunnel release: a machine learning approach.Neurosurgery2022;90:106-13

[49]

Loos NL, Hoogendam L, Souer JS, et al; the Hand-Wrist Study Group. Machine learning can be used to predict function but not pain after surgery for thumb carpometacarpal osteoarthritis. Clin Orthop Relat Res 2022;480:1271-84. PMCID:PMC9191288

[50]

Kim J,Lee YN.Predicting the severity of postoperative scars using artificial intelligence based on images and clinical data.Sci Rep2023;13:13448 PMCID:PMC10439171

[51]

Squiers JJ,Bastawros DS.Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing.J Vasc Surg2022;75:279-85 PMCID:PMC8712350

[52]

Robb L.Potential for machine learning in burn care.J Burn Care Res2022;43:632-9

[53]

Xue Y,Tan R.Artificial intelligence-assisted bioinformatics, microneedle, and diabetic wound healing: a “new deal” of an old drug.ACS Appl Mater Interfaces2022;14:37396-409

[54]

Chae MP,McMenamin PG,Spychal RT.Emerging applications of bedside 3D printing in plastic surgery.Front Surg2015;2:25 PMCID:PMC4468745

[55]

Knoops PGM,Ruggiero F.A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.PLoS One2018;13:e0197209 PMCID:PMC5942840

[56]

Huff TJ,Zuniga JM.The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning.Expert Rev Med Devices2018;15:349-56

[57]

Booth J,Zafeiriou S,Dunaway D.A 3D morphable model learnt from 10,000 faces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, USA. IEEE; 2016. pp. 5543-52.

[58]

Dai H,Smith W.A 3D morphable model of craniofacial shape and texture variation. In: 2017 IEEE International Conference on Computer Vision (ICCV); 2017 Oct 22-29; Venice, Italy. IEEE; 2017. pp. 3104-12.

[59]

Goh GD,Yeong WY.A review on machine learning in 3D printing: applications, potential, and challenges.Artif Intell Rev2021;54:63-94

[60]

Menon A,Feinberg AW.Optimization of silicone 3D printing with hierarchical machine learning.3D Print Addit Manuf2019;6:181-9

[61]

Conev A,Perez MR,Mikos AG.Machine learning-guided three-dimensional printing of tissue engineering scaffolds.Tissue Eng Part A2020;26:1359-68 PMCID:PMC7759288

[62]

Chae MP,Spychal RT.3D volumetric analysis for planning breast reconstructive surgery.Breast Cancer Res Treat2014;146:457-60

[63]

Lei IM,Lei CL.3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients.Nat Commun2021;12:6260 PMCID:PMC8556326

[64]

Asghari A,Attalla P.Game changers: plastic and reconstructive surgery innovations of the last 100 years.Plast Reconstr Surg Glob Open2023;11:e5209 PMCID:PMC10431564

[65]

Lao WWK,Ramirez AE.Differences and similarities between eastern and western rhinoplasty: features and proposed algorithms.Ann Plast Surg2021;86:S259-64 PMCID:PMC7969162

[66]

Mir MA.Precision and progress: machine learning advancements in plastic surgery.Cureus2023;15:e41952 PMCID:PMC10426385

[67]

Pool C,Lighthall JG.Utilizing virtual surgical planning and patient-specific cutting guides in microtia repair with autologous costal cartilage graft.Plast Reconstr Surg2024;154:569e-72e

[68]

O’Sullivan S,Holzinger A.Operational framework and training standard requirements for AI‐empowered robotic surgery.Int J Med Robot2020;16:1-13

[69]

Koçak B,dos Santos DP,Ugga L.Must-have qualities of clinical research on artificial intelligence and machine learning.Balkan Med J2023;40:3-12 PMCID:PMC9874249

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